Mar 8, 2017

Before Technology and Data Change Organizational Culture, They Reflect It

Since the dawn of the internet age, observers have commented on technology's and data's transformational power.

But, as the following article explains, it may be that the most significant cultural, psychological, organizational and managerial insights forward-thinking enterprises can glean from that trove of innovation and knowledge are about those deeply embedded beliefs, fears, biases and misperceptions that prevent organizations from optimizing their potential. JL

Greg Satell comments in Digital Tonto:

As innovation accelerates and we increasingly need to collaborate
with machines, we will not only enhance our abilities, but also reveal
our biases and our faults. In many cases, we will not like what we see. So
maybe we shouldn’t be so quick to blame algorithms. When we don’t like
what we see, it is possible that we are looking at the culture we have
created.In Weapons of Math Destruction, mathematician and data scientist Cathy O’Neil paints a disturbing picture of how data can go awry. “Black box” algorithms that make decisions with little to no transparency or accountability can lead to bizarre situations in which judgments are handed down with no possibility of appeal.
For example, she tells the story of Sarah Wysocki, a teacher who, despite being widely respected by her students, their parents and her peers, was fired because she performed poorly according to an algorithm. She now works at another school district that uses humans to evaluate teachers.
Yet Cava Grill, a restaurant chain similar to Chipotle but focused on healthy Mediterranean cuisine, shows that the problem really isn’t with data or algorithms, but with us. The firm has built a strong culture around data even among its front line employees. The secret, as it turns out, has nothing to do with technology, but what your culture is like to begin with.

Building The Cava Culture

In 2006, three lifelong friends, Ike Grigoropoulos, Ted Xenohristos, and Dimitri Moshovitis, opened Cava Mezze, an elegant full-service restaurant in Washington D.C. It was so successful that they soon began branching out to new locations and selling their popular dips and spreads at high end grocery stores like Whole Foods.
As their business grew, they brought in an experienced executive, Brett Schulman, to help run the packaged goods business. Schulman quickly hit it off with the founders and before long, they started talking about expanding into a fast healthy format. That’s what led to Cava Grill, which opened its first location in January of 2011, with Schulman as CEO.
From the start, the company focused on infusing the friendly atmosphere that began with the founders. Wages at Cava Grill start at $13 an hour, all employees get paid sick days, paid parental leave, and one paid shift day every year to participate in community projects. Its training program, Cava You, offers instruction in skills like communication, personal finance and health and wellness.
“We believe food is a visceral human experience,” Schulman told me, “So we want to infuse our culture with the values that create the kind of environment our customers will appreciate.” The strategy paid off and by 2013, the business began to take off. That’s when Schulman decided that to take Cava Grill to the next level, he needed to hire a data scientist.

Infusing Operations With Data

When Josh Patchus, the first data hire, started his new job at Cava Grill, it became immediately clear that things were quite a bit different than the digital media companies he had previously worked at. For starters, he was required to put in time working the line at the restaurants, side-by-side with hourly employees.
He also quickly realized that he didn’t have anything like the access to data that he had in a purely digital business. He soon got to work changing that, transforming the data architecture from the relatively low-tech operation that most restaurants get by with to a sophisticated end-to-end platform that included sensors in each location linked to powerful analytics software.
Yet he soon also found that implementing data science at Cava Grill was going take a different approach. However, the time he spent working the line in restaurants, as well as the culture that Schulman and the founders created, made it easy for him to work hand-in-hand with restaurant managers, front-line employees and other functional areas of the company.
“We consider our tech team to be essentially a product team and we feel it’s really important everybody partners to build the best experiences for our customers,” CEO Schulman told me. “If any link in the chain is broken, everything goes off the rails really fast, so we work really hard to keep everybody tightly integrated.”

Designing Better Experiences

The data that Patchus and his technology team work with at Cava Grill goes far beyond the ordinary point-of-sale and inventory metrics that most restaurant chains use. There are sensors that detect a wide array of factors such as light, sound, and seating throughout each location. This lets the data team continually run experiments to improve the customer experience.
For example, in one experiment the data team worked with store managers to get the line to move more efficiently. As it turns out, it makes a big difference which personality types you put at each station. When you put people who are more conversational at the beginning, it helps to avoid the line backing up further down and makes everything work more smoothly.
In another experiment, they noticed that customers liked to sit where there was more light, so would gravitate toward the windows on sunny days and toward the interior on rainy days. That insight helps restaurant managers to adjust the lighting according to the weather. These might seem like small things, but over time, they add up.
Interestingly, Cava Grill uses data to help improve employees experience as well. When it came up that employees, especially those with long commutes, hated working short shifts, the data team redesigned the scheduling software to take into account quality of life. It saved the company $500,000 in retraining costs and productivity losses.
What makes these experiments work is that they are not only tested in computer simulations, but the employees themselves give feedback and offer suggestions. In effect, they don’t resent the data team because they are an integral part of how it functions..

Data And Technology Are Best Used To Extend Capabilities, Not Replace Them

Let’s return to to the story of Sarah Wysocki, the teacher who was fired by an algorithm. In light of what’s been done at the Cava Grill, it becomes clear that the algorithm was not the problem. The algorithm was designed by humans, implemented by humans and, most importantly, was given the power to rate teachers by humans.
In other words, data didn’t create the culture that led to Wysocki’s firing, that existed long before. It’s hard to imagine how the story would be different if she was evaluated by a human consultant that ignored the opinions of her peers, her students and their parents. Unfortunately, this kind of data mismanagement is fairly widespread, even among highly trained professionals.
So what’s interesting about Cava Grill is how the use of data and algorithms also deepened the culture, but for the better. “What’s been essential is that our data science team has built genuine partnerships with our other teams with the intention of extending the capabilities of every facet of our business, rather than trying to replace human talents with a set of algorithms,” Schulman told me.
And that’s what’s really crucial to learn from Cava Grill. Technology is, to paraphrase Marshall McLuhan, an extension of man. As innovation accelerates and we increasingly need to collaborate with machines, we will not only enhance our abilities, but also reveal our biases and our faults. In many cases, we will not like what we see.
So maybe we shouldn’t be so quick to blame algorithms. When we don’t like what we see, it is possible that we are looking at the culture we have created.

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As a Partner and Co-Founder of Predictiv and PredictivAsia, Jon specializes in management performance and organizational effectiveness for both domestic and international clients. He is an editor and author whose works include Invisible Advantage: How Intangilbles are Driving Business Performance.Learn more...